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Background: Atrial fibrillation (AF) occurs in up to 20% of patients with Brugada syndrome (BrS), yet its risk factors and prognostic implications remain uncertain.
Objectives: This study sought to identify risk factors for AF in patients with non-high-risk BrS and to evaluate the impact of AF on ventricular arrhythmias (VAs), sick sinus syndrome (SSS), and stroke in non-high-risk BrS.
Methods: This was a multicenter, retrospective study conducted across 20 international centers. Non-high-risk BrS patients were stratified based on the presence or absence of AF. The primary endpoint was the occurrence of VAs, defined as sustained ventricular tachycardia, ventricular fibrillation, or arrhythmic sudden cardiac death.
Results: A total of 686 BrS patients were analyzed (39.3 years of age, 33.1% female, 31.8% spontaneous type 1 electrocardiogram, 36.0% pathogenic/likely pathogenic SCN5A variant), including 280 with AF (40.8%). Proband status and older age were associated with AF at Cox regression analysis. Over a median follow-up of 48.8 months, the incidence of VAs was 0.26% per year, with no significant difference between patients with and without AF (HR: 0.67; P = 0.58). Early-onset AF (<20 years) was associated with significantly higher risk of VAs (P < 0.001). SSS was twice as prevalent in BrS patients with AF (10.0% vs 6.2%; P = 0.047), and stroke occurred exclusively in the AF group (2.5%), despite low CHADS-VA (mean 0.5).
Conclusions: The presence of AF in non-high-risk BrS does not identify patients with higher risk of VAs. However, early-onset AF (<20 years) defines a distinct subgroup with elevated risk. Patients with AF and BrS have a significantly higher risk of SSS and stroke.
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http://dx.doi.org/10.1016/j.jacep.2025.06.031 | DOI Listing |
Am J Med Sci
September 2025
The Ruth and Bruce Rappaport Faculty of Medicine, Technion, Israel Institute of Technology, Haifa, Israel; Department of Internal Medicine, Lady Davis Carmel Medical Center, Haifa, Israel.
Objective: Multifocal atrial tachycardia (MAT), characterized by an irregularly irregular rhythm, is often regarded as a clinical imitator of atrial fibrillation (AF). We aimed to evaluate the prevalence of MAT misclassification as AF in the emergency department (ED) setting.
Methods: A retrospective analysis of 1,828 ECGs from patients discharged with AF diagnoses over five years.
Environ Res
September 2025
Department of Environmental Health, Harvard TH Chan School of Public Health, Boston, MA, USA; Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA.
Background: Fine particulate matter (PM) has been previously linked to cardiovascular diseases (CVDs). PM is a mixture of components, each of which has its own toxicity profile which are not yet well understood. This study explores the relationship between long-term exposure to PM components and hospital admissions with CVDs in the Medicare population.
View Article and Find Full Text PDFClin Neurol Neurosurg
September 2025
Department of Neurology, UPMC, Pittsburgh, PA, USA. Electronic address:
Background: Final infarct volume (FIV) is a strong predictor of stroke outcomes. Although smaller FIV are associated with better outcomes, many patients fail to achieve functional independence. We aimed to identify poor outcome predictors in patients with anterior large vessel occlusion stroke (LVOS) who underwent mechanical thrombectomy (MT) and had small FIV.
View Article and Find Full Text PDFJ Physiol
September 2025
Undergraduate Medical Education, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
J Am Coll Cardiol
August 2025
Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Cardiology, Kaiser Permanente Santa Clara Medical Center, Santa Clara, California, USA. Electronic address:
Background: Accurate measurement of echocardiographic parameters is crucial for the diagnosis of cardiovascular disease and tracking of change over time; however, manual assessment requires time-consuming effort and can be imprecise. Artificial intelligence has the potential to reduce clinician burden by automating the time-intensive task of comprehensive measurement of echocardiographic parameters.
Objectives: The purpose of this study was to develop and validate open-sourced deep learning semantic segmentation models for the automated measurement of 18 anatomic and Doppler measurements in echocardiography.